Explain ML model predictions by attributing outputs to features using SHAP values.
npx skills add https://github.com/OwnLabAI/ownlab --skill shap-ownlabai
Please help me install this Agent Skill. Skill: shap Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/shap Command: npx skills add https://github.com/OwnLabAI/ownlab --skill shap-ownlabai
SHAP-based explanations provide a principled attribution of a model's prediction to its input features, enabling transparent understanding of why decisions are made and what drives outputs.
Train a model, create an appropriate SHAP explainer, compute SHAP values on sample data, and visualize explanations with standard plots.
High-intent search queries and answers about installing and using this skill.
Feature attribution explains model predictions by assigning values to inputs using SHAP values. This Skill computes attributions across tree-based, deep learning, and linear models to estimate feature importance and generate explanation dashboards.
Visualizing SHAP values for non-technical audiences is best done using standard plots like waterfall, beeswarm, bar, and force plots. This Skill provides a visualization suite to communicate model insights and feature attributions clearly.
Yes, SHAP explainers support both classification and regression tasks. This Skill applies TreeExplainer, DeepExplainer, LinearExplainer, or KernelExplainer depending on your model architecture to compute accurate feature attributions.
Computing SHAP values for deep learning models requires using the DeepExplainer to attribute outputs to features. This Skill handles baseline computation and background data to provide accurate feature importance estimations for neural networks.
Yes, SHAP attributions enable model debugging and fairness analysis by providing transparent understanding of why decisions are made. This Skill generates global and local explanations to identify biased features and compare model behaviors.
TreeExplainer is used for tree-based models while KernelExplainer applies to other model types for model interpretation. This Skill selects the appropriate explainer based on your model architecture to compute accurate SHAP values.
dralkhCompute SHAP values and generate feature attribution plots for machine learning models.
youyinnnExplain machine learning model predictions using SHAP values and plots.
Weiwei-MaoCompute SHAP feature attributions and generate explanation plots for machine learning models.
tondevrelAnalyze scikit-learn model feature importance with SHAP, LIME, and partial dependence plots.
